{"id": "chat-aefd82557288405f8c402ebbd2566587", "object": "chat.completion", "created": 1737100177, "model": "Qwen2.5-7B-Instruct", "choices": [{"index": 0, "message": {"role": "assistant", "content": "To complete the function `has_close_elements`, we can sort the list of numbers first and then check the difference between consecutive elements. If any two consecutive elements have a difference less than the threshold, we return `True`. Otherwise, we return `False` after checking all pairs.\n\nHere is the completed code:\n\n```python\nfrom typing import List\n\ndef has_close_elements(numbers: List[float], threshold: float) -> bool:\n    \"\"\" Check if in given list of numbers, are any two numbers closer to each other than\n    given threshold.\n    >>> has_close_elements([1.0, 2.0, 3.0], 0.5)\n    False\n    >>> has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3)\n    True\n    \"\"\"\n    # Sort the list of numbers\n    numbers.sort()\n    \n    # Check the difference between consecutive elements\n    for i in range(len(numbers) - 1):\n        if numbers[i + 1] - numbers[i] < threshold:\n            return True\n    \n    return False\n\n# Test cases\nprint(has_close_elements([1.0, 2.0, 3.0], 0.5))  # Output: False\nprint(has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3))  # Output: True\n```\n\nThis code first sorts the list of numbers to ensure that we only need to check consecutive elements for the closest pair. Then it iterates through the sorted list and checks if the difference between any two consecutive elements is less than the given threshold. If such a pair is found, it returns `True`; otherwise, it returns `False` after checking all pairs.", "tool_calls": []}, "logprobs": null, "finish_reason": "stop", "stop_reason": null}], "usage": {"prompt_tokens": 153, "total_tokens": 547, "completion_tokens": 394}, "prompt_logprobs": null, "model_spec": {"api_url": "http://127.0.0.1:8801/v1/chat/completions", "model_id": "Qwen2.5-7B-Instruct", "api_key": "EMPTY"}, "answer_id": "answer-33cb4f64a112ecf4988c95c94efa5545", "subset_name": "openai_humaneval", "raw_input": {"task_id": "HumanEval/0", "prompt": "from typing import List\n\n\ndef has_close_elements(numbers: List[float], threshold: float) -> bool:\n    \"\"\" Check if in given list of numbers, are any two numbers closer to each other than\n    given threshold.\n    >>> has_close_elements([1.0, 2.0, 3.0], 0.5)\n    False\n    >>> has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3)\n    True\n    \"\"\"\n", "canonical_solution": "    for idx, elem in enumerate(numbers):\n        for idx2, elem2 in enumerate(numbers):\n            if idx != idx2:\n                distance = abs(elem - elem2)\n                if distance < threshold:\n                    return True\n\n    return False\n", "test": "\n\nMETADATA = {\n    'author': 'jt',\n    'dataset': 'test'\n}\n\n\ndef check(candidate):\n    assert candidate([1.0, 2.0, 3.9, 4.0, 5.0, 2.2], 0.3) == True\n    assert candidate([1.0, 2.0, 3.9, 4.0, 5.0, 2.2], 0.05) == False\n    assert candidate([1.0, 2.0, 5.9, 4.0, 5.0], 0.95) == True\n    assert candidate([1.0, 2.0, 5.9, 4.0, 5.0], 0.8) == False\n    assert candidate([1.0, 2.0, 3.0, 4.0, 5.0, 2.0], 0.1) == True\n    assert candidate([1.1, 2.2, 3.1, 4.1, 5.1], 1.0) == True\n    assert candidate([1.1, 2.2, 3.1, 4.1, 5.1], 0.5) == False\n\n", "entry_point": "has_close_elements"}, "origin_prompt": {"data": ["Complete the following python code:\n\nfrom typing import List\n\n\ndef has_close_elements(numbers: List[float], threshold: float) -> bool:\n    \"\"\" Check if in given list of numbers, are any two numbers closer to each other than\n    given threshold.\n    >>> has_close_elements([1.0, 2.0, 3.0], 0.5)\n    False\n    >>> has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3)\n    True\n    \"\"\"\n"], "raw_input": {"task_id": "HumanEval/0", "prompt": "from typing import List\n\n\ndef has_close_elements(numbers: List[float], threshold: float) -> bool:\n    \"\"\" Check if in given list of numbers, are any two numbers closer to each other than\n    given threshold.\n    >>> has_close_elements([1.0, 2.0, 3.0], 0.5)\n    False\n    >>> has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3)\n    True\n    \"\"\"\n", "canonical_solution": "    for idx, elem in enumerate(numbers):\n        for idx2, elem2 in enumerate(numbers):\n            if idx != idx2:\n                distance = abs(elem - elem2)\n                if distance < threshold:\n                    return True\n\n    return False\n", "test": "\n\nMETADATA = {\n    'author': 'jt',\n    'dataset': 'test'\n}\n\n\ndef check(candidate):\n    assert candidate([1.0, 2.0, 3.9, 4.0, 5.0, 2.2], 0.3) == True\n    assert candidate([1.0, 2.0, 3.9, 4.0, 5.0, 2.2], 0.05) == False\n    assert candidate([1.0, 2.0, 5.9, 4.0, 5.0], 0.95) == True\n    assert candidate([1.0, 2.0, 5.9, 4.0, 5.0], 0.8) == False\n    assert candidate([1.0, 2.0, 3.0, 4.0, 5.0, 2.0], 0.1) == True\n    assert candidate([1.1, 2.2, 3.1, 4.1, 5.1], 1.0) == True\n    assert candidate([1.1, 2.2, 3.1, 4.1, 5.1], 0.5) == False\n\n", "entry_point": "has_close_elements"}}}
{"id": "chat-fb8807ffc7e645b6bd1c98f7f98394ed", "object": "chat.completion", "created": 1737100182, "model": "Qwen2.5-7B-Instruct", "choices": [{"index": 0, "message": {"role": "assistant", "content": "To complete the function `separate_paren_groups`, we need to parse the input string and identify separate groups of balanced parentheses. We can achieve this by iterating through the string and keeping track of the balance of parentheses. When we encounter a balanced group, we can add it to our result list. Here's the complete code:\n\n```python\nfrom typing import List\n\ndef separate_paren_groups(paren_string: str) -> List[str]:\n    \"\"\" Input to this function is a string containing multiple groups of nested parentheses. Your goal is to\n    separate those groups into separate strings and return the list of those.\n    Separate groups are balanced (each open brace is properly closed) and not nested within each other\n    Ignore any spaces in the input string.\n    \"\"\"\n    paren_string = paren_string.replace(\" \", \"\")  # Remove spaces\n    result = []\n    balance = 0\n    start_index = 0\n\n    for i, char in enumerate(paren_string):\n        if char == '(':\n            if balance == 0:\n                start_index = i  # Start of a new group\n            balance += 1\n        elif char == ')':\n            balance -= 1\n            if balance == 0:\n                result.append(paren_string[start_index:i+1])  # End of a group\n\n    return result\n\n# Test the function\nprint(separate_paren_groups('( ) (( )) (( )( ))'))  # Output: ['()', '(())', '(()())']\n```\n\n### Explanation:\n1. **Remove Spaces**: We first remove any spaces from the input string using `paren_string.replace(\" \", \"\")`.\n2. **Initialize Variables**: We initialize `balance` to keep track of the balance of parentheses and `start_index` to mark the start of a new group.\n3. **Iterate Through the String**: We iterate through each character in the string.\n   - If we encounter an opening parenthesis `(` and the balance is 0, it means we've just started a new group, so we record the start index.\n   - We increment the balance for an opening parenthesis.\n   - If we encounter a closing parenthesis `)` and the balance is 1, it means we've just closed a group, so we record the end of the group and add it to the result list.\n   - We decrement the balance for a closing parenthesis.\n4. **Return the Result**: Finally, we return the list of separated groups.\n\nThis approach ensures that we correctly identify and separate each group of balanced parentheses.", "tool_calls": []}, "logprobs": null, "finish_reason": "stop", "stop_reason": null}], "usage": {"prompt_tokens": 144, "total_tokens": 656, "completion_tokens": 512}, "prompt_logprobs": null, "model_spec": {"api_url": "http://127.0.0.1:8801/v1/chat/completions", "model_id": "Qwen2.5-7B-Instruct", "api_key": "EMPTY"}, "answer_id": "answer-2a84def173607f1e414f65ac94d3084f", "subset_name": "openai_humaneval", "raw_input": {"task_id": "HumanEval/1", "prompt": "from typing import List\n\n\ndef separate_paren_groups(paren_string: str) -> List[str]:\n    \"\"\" Input to this function is a string containing multiple groups of nested parentheses. Your goal is to\n    separate those group into separate strings and return the list of those.\n    Separate groups are balanced (each open brace is properly closed) and not nested within each other\n    Ignore any spaces in the input string.\n    >>> separate_paren_groups('( ) (( )) (( )( ))')\n    ['()', '(())', '(()())']\n    \"\"\"\n", "canonical_solution": "    result = []\n    current_string = []\n    current_depth = 0\n\n    for c in paren_string:\n        if c == '(':\n            current_depth += 1\n            current_string.append(c)\n        elif c == ')':\n            current_depth -= 1\n            current_string.append(c)\n\n            if current_depth == 0:\n                result.append(''.join(current_string))\n                current_string.clear()\n\n    return result\n", "test": "\n\nMETADATA = {\n    'author': 'jt',\n    'dataset': 'test'\n}\n\n\ndef check(candidate):\n    assert candidate('(()()) ((())) () ((())()())') == [\n        '(()())', '((()))', '()', '((())()())'\n    ]\n    assert candidate('() (()) ((())) (((())))') == [\n        '()', '(())', '((()))', '(((())))'\n    ]\n    assert candidate('(()(())((())))') == [\n        '(()(())((())))'\n    ]\n    assert candidate('( ) (( )) (( )( ))') == ['()', '(())', '(()())']\n", "entry_point": "separate_paren_groups"}, "origin_prompt": {"data": ["Complete the following python code:\n\nfrom typing import List\n\n\ndef separate_paren_groups(paren_string: str) -> List[str]:\n    \"\"\" Input to this function is a string containing multiple groups of nested parentheses. Your goal is to\n    separate those group into separate strings and return the list of those.\n    Separate groups are balanced (each open brace is properly closed) and not nested within each other\n    Ignore any spaces in the input string.\n    >>> separate_paren_groups('( ) (( )) (( )( ))')\n    ['()', '(())', '(()())']\n    \"\"\"\n"], "raw_input": {"task_id": "HumanEval/1", "prompt": "from typing import List\n\n\ndef separate_paren_groups(paren_string: str) -> List[str]:\n    \"\"\" Input to this function is a string containing multiple groups of nested parentheses. Your goal is to\n    separate those group into separate strings and return the list of those.\n    Separate groups are balanced (each open brace is properly closed) and not nested within each other\n    Ignore any spaces in the input string.\n    >>> separate_paren_groups('( ) (( )) (( )( ))')\n    ['()', '(())', '(()())']\n    \"\"\"\n", "canonical_solution": "    result = []\n    current_string = []\n    current_depth = 0\n\n    for c in paren_string:\n        if c == '(':\n            current_depth += 1\n            current_string.append(c)\n        elif c == ')':\n            current_depth -= 1\n            current_string.append(c)\n\n            if current_depth == 0:\n                result.append(''.join(current_string))\n                current_string.clear()\n\n    return result\n", "test": "\n\nMETADATA = {\n    'author': 'jt',\n    'dataset': 'test'\n}\n\n\ndef check(candidate):\n    assert candidate('(()()) ((())) () ((())()())') == [\n        '(()())', '((()))', '()', '((())()())'\n    ]\n    assert candidate('() (()) ((())) (((())))') == [\n        '()', '(())', '((()))', '(((())))'\n    ]\n    assert candidate('(()(())((())))') == [\n        '(()(())((())))'\n    ]\n    assert candidate('( ) (( )) (( )( ))') == ['()', '(())', '(()())']\n", "entry_point": "separate_paren_groups"}}}
{"id": "chat-2f3da3e74b2c4e5f9cc6ea6051ce0533", "object": "chat.completion", "created": 1737100187, "model": "Qwen2.5-7B-Instruct", "choices": [{"index": 0, "message": {"role": "assistant", "content": "To complete the function `truncate_number`, you need to extract the decimal part of the given floating-point number. You can achieve this by subtracting the integer part of the number from the original number. Here's the completed code:\n\n```python\ndef truncate_number(number: float) -> float:\n    \"\"\" Given a positive floating point number, it can be decomposed into\n    and integer part (largest integer smaller than given number) and decimals\n    (leftover part always smaller than 1).\n\n    Return the decimal part of the number.\n    \"\"\"\n    return number - int(number)\n\n# Test the function\nif __name__ == \"__main__\":\n    import doctest\n    doctest.testmod()\n```\n\n### Explanation:\n- `int(number)`: This converts the floating-point number to the largest integer smaller than the given number.\n- `number - int(number)`: This subtraction gives you the decimal part of the number.\n\n### Test Cases:\nThe `doctest` module is used to verify the correctness of the function with the provided example:\n\n```python\n>>> truncate_number(3.5)\n0.5\n```\n\nYou can run the `doctest` by executing the script, and it will automatically check the example provided in the docstring.", "tool_calls": []}, "logprobs": null, "finish_reason": "stop", "stop_reason": null}], "usage": {"prompt_tokens": 114, "total_tokens": 372, "completion_tokens": 258}, "prompt_logprobs": null, "model_spec": {"api_url": "http://127.0.0.1:8801/v1/chat/completions", "model_id": "Qwen2.5-7B-Instruct", "api_key": "EMPTY"}, "answer_id": "answer-30d921ea3f0099c48ec334e609e88aa8", "subset_name": "openai_humaneval", "raw_input": {"task_id": "HumanEval/2", "prompt": "\n\ndef truncate_number(number: float) -> float:\n    \"\"\" Given a positive floating point number, it can be decomposed into\n    and integer part (largest integer smaller than given number) and decimals\n    (leftover part always smaller than 1).\n\n    Return the decimal part of the number.\n    >>> truncate_number(3.5)\n    0.5\n    \"\"\"\n", "canonical_solution": "    return number % 1.0\n", "test": "\n\nMETADATA = {\n    'author': 'jt',\n    'dataset': 'test'\n}\n\n\ndef check(candidate):\n    assert candidate(3.5) == 0.5\n    assert abs(candidate(1.33) - 0.33) < 1e-6\n    assert abs(candidate(123.456) - 0.456) < 1e-6\n", "entry_point": "truncate_number"}, "origin_prompt": {"data": ["Complete the following python code:\n\n\n\ndef truncate_number(number: float) -> float:\n    \"\"\" Given a positive floating point number, it can be decomposed into\n    and integer part (largest integer smaller than given number) and decimals\n    (leftover part always smaller than 1).\n\n    Return the decimal part of the number.\n    >>> truncate_number(3.5)\n    0.5\n    \"\"\"\n"], "raw_input": {"task_id": "HumanEval/2", "prompt": "\n\ndef truncate_number(number: float) -> float:\n    \"\"\" Given a positive floating point number, it can be decomposed into\n    and integer part (largest integer smaller than given number) and decimals\n    (leftover part always smaller than 1).\n\n    Return the decimal part of the number.\n    >>> truncate_number(3.5)\n    0.5\n    \"\"\"\n", "canonical_solution": "    return number % 1.0\n", "test": "\n\nMETADATA = {\n    'author': 'jt',\n    'dataset': 'test'\n}\n\n\ndef check(candidate):\n    assert candidate(3.5) == 0.5\n    assert abs(candidate(1.33) - 0.33) < 1e-6\n    assert abs(candidate(123.456) - 0.456) < 1e-6\n", "entry_point": "truncate_number"}}}
{"id": "chat-94047698e23d44658406a8c15d506d20", "object": "chat.completion", "created": 1737100191, "model": "Qwen2.5-7B-Instruct", "choices": [{"index": 0, "message": {"role": "assistant", "content": "Certainly! Here's the completed Python code for the `below_zero` function:\n\n```python\nfrom typing import List\n\ndef below_zero(operations: List[int]) -> bool:\n    \"\"\" You're given a list of deposit and withdrawal operations on a bank account that starts with\n    zero balance. Your task is to detect if at any point the balance of account falls below zero, and\n    at that point function should return True. Otherwise it should return False.\n    \"\"\"\n    balance = 0\n    for op in operations:\n        balance += op\n        if balance < 0:\n            return True\n    return False\n\n# Test cases\nprint(below_zero([1, 2, 3]))  # Output: False\nprint(below_zero([1, 2, -4, 5]))  # Output: True\n```\n\nThis function initializes the balance to zero and iterates through each operation, updating the balance. If the balance ever falls below zero, it returns `True`. If the loop completes without the balance going below zero, it returns `False`.", "tool_calls": []}, "logprobs": null, "finish_reason": "stop", "stop_reason": null}], "usage": {"prompt_tokens": 148, "total_tokens": 371, "completion_tokens": 223}, "prompt_logprobs": null, "model_spec": {"api_url": "http://127.0.0.1:8801/v1/chat/completions", "model_id": "Qwen2.5-7B-Instruct", "api_key": "EMPTY"}, "answer_id": "answer-e5014ac347362240b98e305ed86231c7", "subset_name": "openai_humaneval", "raw_input": {"task_id": "HumanEval/3", "prompt": "from typing import List\n\n\ndef below_zero(operations: List[int]) -> bool:\n    \"\"\" You're given a list of deposit and withdrawal operations on a bank account that starts with\n    zero balance. Your task is to detect if at any point the balance of account fallls below zero, and\n    at that point function should return True. Otherwise it should return False.\n    >>> below_zero([1, 2, 3])\n    False\n    >>> below_zero([1, 2, -4, 5])\n    True\n    \"\"\"\n", "canonical_solution": "    balance = 0\n\n    for op in operations:\n        balance += op\n        if balance < 0:\n            return True\n\n    return False\n", "test": "\n\nMETADATA = {\n    'author': 'jt',\n    'dataset': 'test'\n}\n\n\ndef check(candidate):\n    assert candidate([]) == False\n    assert candidate([1, 2, -3, 1, 2, -3]) == False\n    assert candidate([1, 2, -4, 5, 6]) == True\n    assert candidate([1, -1, 2, -2, 5, -5, 4, -4]) == False\n    assert candidate([1, -1, 2, -2, 5, -5, 4, -5]) == True\n    assert candidate([1, -2, 2, -2, 5, -5, 4, -4]) == True\n", "entry_point": "below_zero"}, "origin_prompt": {"data": ["Complete the following python code:\n\nfrom typing import List\n\n\ndef below_zero(operations: List[int]) -> bool:\n    \"\"\" You're given a list of deposit and withdrawal operations on a bank account that starts with\n    zero balance. Your task is to detect if at any point the balance of account fallls below zero, and\n    at that point function should return True. Otherwise it should return False.\n    >>> below_zero([1, 2, 3])\n    False\n    >>> below_zero([1, 2, -4, 5])\n    True\n    \"\"\"\n"], "raw_input": {"task_id": "HumanEval/3", "prompt": "from typing import List\n\n\ndef below_zero(operations: List[int]) -> bool:\n    \"\"\" You're given a list of deposit and withdrawal operations on a bank account that starts with\n    zero balance. Your task is to detect if at any point the balance of account fallls below zero, and\n    at that point function should return True. Otherwise it should return False.\n    >>> below_zero([1, 2, 3])\n    False\n    >>> below_zero([1, 2, -4, 5])\n    True\n    \"\"\"\n", "canonical_solution": "    balance = 0\n\n    for op in operations:\n        balance += op\n        if balance < 0:\n            return True\n\n    return False\n", "test": "\n\nMETADATA = {\n    'author': 'jt',\n    'dataset': 'test'\n}\n\n\ndef check(candidate):\n    assert candidate([]) == False\n    assert candidate([1, 2, -3, 1, 2, -3]) == False\n    assert candidate([1, 2, -4, 5, 6]) == True\n    assert candidate([1, -1, 2, -2, 5, -5, 4, -4]) == False\n    assert candidate([1, -1, 2, -2, 5, -5, 4, -5]) == True\n    assert candidate([1, -2, 2, -2, 5, -5, 4, -4]) == True\n", "entry_point": "below_zero"}}}
{"id": "chat-a1681b9b5ea6410fa401f4d7b09c7750", "object": "chat.completion", "created": 1737100193, "model": "Qwen2.5-7B-Instruct", "choices": [{"index": 0, "message": {"role": "assistant", "content": "Certainly! Below is the completed Python code for calculating the Mean Absolute Deviation (MAD) around the mean of a dataset:\n\n```python\nfrom typing import List\n\n\ndef mean_absolute_deviation(numbers: List[float]) -> float:\n    \"\"\" For a given list of input numbers, calculate Mean Absolute Deviation\n    around the mean of this dataset.\n    Mean Absolute Deviation is the average absolute difference between each\n    element and a centerpoint (mean in this case):\n    MAD = average | x - x_mean |\n    \"\"\"\n    # Calculate the mean of the dataset\n    mean_value = sum(numbers) / len(numbers)\n    \n    # Calculate the absolute differences from the mean\n    absolute_differences = [abs(x - mean_value) for x in numbers]\n    \n    # Calculate the average of the absolute differences\n    mad = sum(absolute_differences) / len(numbers)\n    \n    return mad\n\n\n# Example usage:\nif __name__ == \"__main__\":\n    print(mean_absolute_deviation([1.0, 2.0, 3.0, 4.0]))  # Output: 1.0\n```\n\nThis code defines the `mean_absolute_deviation` function, which calculates the mean of the input list, computes the absolute differences between each element and the mean, and then calculates the average of these absolute differences. The example usage at the bottom demonstrates how to call the function and print the result.", "tool_calls": []}, "logprobs": null, "finish_reason": "stop", "stop_reason": null}], "usage": {"prompt_tokens": 146, "total_tokens": 440, "completion_tokens": 294}, "prompt_logprobs": null, "model_spec": {"api_url": "http://127.0.0.1:8801/v1/chat/completions", "model_id": "Qwen2.5-7B-Instruct", "api_key": "EMPTY"}, "answer_id": "answer-736c7d291489ffceb045c7d6ce1eb42b", "subset_name": "openai_humaneval", "raw_input": {"task_id": "HumanEval/4", "prompt": "from typing import List\n\n\ndef mean_absolute_deviation(numbers: List[float]) -> float:\n    \"\"\" For a given list of input numbers, calculate Mean Absolute Deviation\n    around the mean of this dataset.\n    Mean Absolute Deviation is the average absolute difference between each\n    element and a centerpoint (mean in this case):\n    MAD = average | x - x_mean |\n    >>> mean_absolute_deviation([1.0, 2.0, 3.0, 4.0])\n    1.0\n    \"\"\"\n", "canonical_solution": "    mean = sum(numbers) / len(numbers)\n    return sum(abs(x - mean) for x in numbers) / len(numbers)\n", "test": "\n\nMETADATA = {\n    'author': 'jt',\n    'dataset': 'test'\n}\n\n\ndef check(candidate):\n    assert abs(candidate([1.0, 2.0, 3.0]) - 2.0/3.0) < 1e-6\n    assert abs(candidate([1.0, 2.0, 3.0, 4.0]) - 1.0) < 1e-6\n    assert abs(candidate([1.0, 2.0, 3.0, 4.0, 5.0]) - 6.0/5.0) < 1e-6\n\n", "entry_point": "mean_absolute_deviation"}, "origin_prompt": {"data": ["Complete the following python code:\n\nfrom typing import List\n\n\ndef mean_absolute_deviation(numbers: List[float]) -> float:\n    \"\"\" For a given list of input numbers, calculate Mean Absolute Deviation\n    around the mean of this dataset.\n    Mean Absolute Deviation is the average absolute difference between each\n    element and a centerpoint (mean in this case):\n    MAD = average | x - x_mean |\n    >>> mean_absolute_deviation([1.0, 2.0, 3.0, 4.0])\n    1.0\n    \"\"\"\n"], "raw_input": {"task_id": "HumanEval/4", "prompt": "from typing import List\n\n\ndef mean_absolute_deviation(numbers: List[float]) -> float:\n    \"\"\" For a given list of input numbers, calculate Mean Absolute Deviation\n    around the mean of this dataset.\n    Mean Absolute Deviation is the average absolute difference between each\n    element and a centerpoint (mean in this case):\n    MAD = average | x - x_mean |\n    >>> mean_absolute_deviation([1.0, 2.0, 3.0, 4.0])\n    1.0\n    \"\"\"\n", "canonical_solution": "    mean = sum(numbers) / len(numbers)\n    return sum(abs(x - mean) for x in numbers) / len(numbers)\n", "test": "\n\nMETADATA = {\n    'author': 'jt',\n    'dataset': 'test'\n}\n\n\ndef check(candidate):\n    assert abs(candidate([1.0, 2.0, 3.0]) - 2.0/3.0) < 1e-6\n    assert abs(candidate([1.0, 2.0, 3.0, 4.0]) - 1.0) < 1e-6\n    assert abs(candidate([1.0, 2.0, 3.0, 4.0, 5.0]) - 6.0/5.0) < 1e-6\n\n", "entry_point": "mean_absolute_deviation"}}}
{"id": "chat-b19ff6fd016f427b82c9532d2d772cd0", "object": "chat.completion", "created": 1737100197, "model": "Qwen2.5-7B-Instruct", "choices": [{"index": 0, "message": {"role": "assistant", "content": "Certainly! To achieve the desired functionality, you can iterate through the list and insert the delimiter between each pair of consecutive elements. Here's the completed code:\n\n```python\nfrom typing import List\n\n\ndef intersperse(numbers: List[int], delimiter: int) -> List[int]:\n    \"\"\" Insert a number 'delimiter' between every two consecutive elements of input list `numbers`\n    >>> intersperse([], 4)\n    []\n    >>> intersperse([1, 2, 3], 4)\n    [1, 4, 2, 4, 3]\n    \"\"\"\n    result = []\n    for i, num in enumerate(numbers):\n        result.append(num)\n        if i < len(numbers) - 1:\n            result.append(delimiter)\n    return result\n\n# Test cases\nprint(intersperse([], 4))  # Output: []\nprint(intersperse([1, 2, 3], 4))  # Output: [1, 4, 2, 4, 3]\n```\n\nThis function works by iterating over the input list `numbers` and appending each element to the `result` list. If the current index `i` is less than the length of `numbers` minus one, it also appends the `delimiter` to the `result` list. This ensures that the delimiter is inserted between every two consecutive elements.", "tool_calls": []}, "logprobs": null, "finish_reason": "stop", "stop_reason": null}], "usage": {"prompt_tokens": 121, "total_tokens": 403, "completion_tokens": 282}, "prompt_logprobs": null, "model_spec": {"api_url": "http://127.0.0.1:8801/v1/chat/completions", "model_id": "Qwen2.5-7B-Instruct", "api_key": "EMPTY"}, "answer_id": "answer-8c35d350c40c275cd38663f31f93ddfc", "subset_name": "openai_humaneval", "raw_input": {"task_id": "HumanEval/5", "prompt": "from typing import List\n\n\ndef intersperse(numbers: List[int], delimeter: int) -> List[int]:\n    \"\"\" Insert a number 'delimeter' between every two consecutive elements of input list `numbers'\n    >>> intersperse([], 4)\n    []\n    >>> intersperse([1, 2, 3], 4)\n    [1, 4, 2, 4, 3]\n    \"\"\"\n", "canonical_solution": "    if not numbers:\n        return []\n\n    result = []\n\n    for n in numbers[:-1]:\n        result.append(n)\n        result.append(delimeter)\n\n    result.append(numbers[-1])\n\n    return result\n", "test": "\n\nMETADATA = {\n    'author': 'jt',\n    'dataset': 'test'\n}\n\n\ndef check(candidate):\n    assert candidate([], 7) == []\n    assert candidate([5, 6, 3, 2], 8) == [5, 8, 6, 8, 3, 8, 2]\n    assert candidate([2, 2, 2], 2) == [2, 2, 2, 2, 2]\n", "entry_point": "intersperse"}, "origin_prompt": {"data": ["Complete the following python code:\n\nfrom typing import List\n\n\ndef intersperse(numbers: List[int], delimeter: int) -> List[int]:\n    \"\"\" Insert a number 'delimeter' between every two consecutive elements of input list `numbers'\n    >>> intersperse([], 4)\n    []\n    >>> intersperse([1, 2, 3], 4)\n    [1, 4, 2, 4, 3]\n    \"\"\"\n"], "raw_input": {"task_id": "HumanEval/5", "prompt": "from typing import List\n\n\ndef intersperse(numbers: List[int], delimeter: int) -> List[int]:\n    \"\"\" Insert a number 'delimeter' between every two consecutive elements of input list `numbers'\n    >>> intersperse([], 4)\n    []\n    >>> intersperse([1, 2, 3], 4)\n    [1, 4, 2, 4, 3]\n    \"\"\"\n", "canonical_solution": "    if not numbers:\n        return []\n\n    result = []\n\n    for n in numbers[:-1]:\n        result.append(n)\n        result.append(delimeter)\n\n    result.append(numbers[-1])\n\n    return result\n", "test": "\n\nMETADATA = {\n    'author': 'jt',\n    'dataset': 'test'\n}\n\n\ndef check(candidate):\n    assert candidate([], 7) == []\n    assert candidate([5, 6, 3, 2], 8) == [5, 8, 6, 8, 3, 8, 2]\n    assert candidate([2, 2, 2], 2) == [2, 2, 2, 2, 2]\n", "entry_point": "intersperse"}}}
{"id": "chat-44b8d179d7b741ba960d4cf8e8d01cd9", "object": "chat.completion", "created": 1737100200, "model": "Qwen2.5-7B-Instruct", "choices": [{"index": 0, "message": {"role": "assistant", "content": "To complete the function `parse_nested_parens`, we need to iterate through each group of parentheses, count the maximum depth of nesting for each group, and then return a list of these maximum depths. Here's the complete code:\n\n```python\nfrom typing import List\n\ndef parse_nested_parens(paren_string: str) -> List[int]:\n    \"\"\" Input to this function is a string represented multiple groups for nested parentheses separated by spaces.\n    For each of the group, output the deepest level of nesting of parentheses.\n    E.g. (()()) has maximum two levels of nesting while ((())) has three.\n\n    >>> parse_nested_parentheses('(()()) ((())) () ((())()())')\n    [2, 3, 1, 3]\n    \"\"\"\n    def max_nesting_depth(group: str) -> int:\n        max_depth = 0\n        current_depth = 0\n        for char in group:\n            if char == '(':\n                current_depth += 1\n                max_depth = max(max_depth, current_depth)\n            elif char == ')':\n                current_depth -= 1\n        return max_depth\n\n    groups = paren_string.split()\n    return [max_nesting_depth(group) for group in groups]\n\n# Test the function\nprint(parse_nested_parens('(()()) ((())) () ((())()())'))\n```\n\n### Explanation:\n1. **Helper Function `max_nesting_depth`**:\n   - This function takes a string `group` and returns the maximum depth of nesting of parentheses in that group.\n   - It initializes `max_depth` and `current_depth` to 0.\n   - It iterates through each character in the group:\n     - If the character is `(`, it increments `current_depth` and updates `max_depth` if `current_depth` is greater.\n     - If the character is `)`, it decrements `current_depth`.\n   - Finally, it returns `max_depth`.\n\n2. **Main Function `parse_nested_parens`**:\n   - This function splits the input string into groups based on spaces.\n   - It then applies the `max_nesting_depth` function to each group and collects the results in a list.\n   - It returns this list of maximum depths.\n\n3. **Testing**:\n   - The function is tested with the provided example input to verify its correctness.", "tool_calls": []}, "logprobs": null, "finish_reason": "stop", "stop_reason": null}], "usage": {"prompt_tokens": 141, "total_tokens": 625, "completion_tokens": 484}, "prompt_logprobs": null, "model_spec": {"api_url": "http://127.0.0.1:8801/v1/chat/completions", "model_id": "Qwen2.5-7B-Instruct", "api_key": "EMPTY"}, "answer_id": "answer-0dd5979bbe18e4a59f6f4fe95d90b67c", "subset_name": "openai_humaneval", "raw_input": {"task_id": "HumanEval/6", "prompt": "from typing import List\n\n\ndef parse_nested_parens(paren_string: str) -> List[int]:\n    \"\"\" Input to this function is a string represented multiple groups for nested parentheses separated by spaces.\n    For each of the group, output the deepest level of nesting of parentheses.\n    E.g. (()()) has maximum two levels of nesting while ((())) has three.\n\n    >>> parse_nested_parens('(()()) ((())) () ((())()())')\n    [2, 3, 1, 3]\n    \"\"\"\n", "canonical_solution": "    def parse_paren_group(s):\n        depth = 0\n        max_depth = 0\n        for c in s:\n            if c == '(':\n                depth += 1\n                max_depth = max(depth, max_depth)\n            else:\n                depth -= 1\n\n        return max_depth\n\n    return [parse_paren_group(x) for x in paren_string.split(' ') if x]\n", "test": "\n\nMETADATA = {\n    'author': 'jt',\n    'dataset': 'test'\n}\n\n\ndef check(candidate):\n    assert candidate('(()()) ((())) () ((())()())') == [2, 3, 1, 3]\n    assert candidate('() (()) ((())) (((())))') == [1, 2, 3, 4]\n    assert candidate('(()(())((())))') == [4]\n", "entry_point": "parse_nested_parens"}, "origin_prompt": {"data": ["Complete the following python code:\n\nfrom typing import List\n\n\ndef parse_nested_parens(paren_string: str) -> List[int]:\n    \"\"\" Input to this function is a string represented multiple groups for nested parentheses separated by spaces.\n    For each of the group, output the deepest level of nesting of parentheses.\n    E.g. (()()) has maximum two levels of nesting while ((())) has three.\n\n    >>> parse_nested_parens('(()()) ((())) () ((())()())')\n    [2, 3, 1, 3]\n    \"\"\"\n"], "raw_input": {"task_id": "HumanEval/6", "prompt": "from typing import List\n\n\ndef parse_nested_parens(paren_string: str) -> List[int]:\n    \"\"\" Input to this function is a string represented multiple groups for nested parentheses separated by spaces.\n    For each of the group, output the deepest level of nesting of parentheses.\n    E.g. (()()) has maximum two levels of nesting while ((())) has three.\n\n    >>> parse_nested_parens('(()()) ((())) () ((())()())')\n    [2, 3, 1, 3]\n    \"\"\"\n", "canonical_solution": "    def parse_paren_group(s):\n        depth = 0\n        max_depth = 0\n        for c in s:\n            if c == '(':\n                depth += 1\n                max_depth = max(depth, max_depth)\n            else:\n                depth -= 1\n\n        return max_depth\n\n    return [parse_paren_group(x) for x in paren_string.split(' ') if x]\n", "test": "\n\nMETADATA = {\n    'author': 'jt',\n    'dataset': 'test'\n}\n\n\ndef check(candidate):\n    assert candidate('(()()) ((())) () ((())()())') == [2, 3, 1, 3]\n    assert candidate('() (()) ((())) (((())))') == [1, 2, 3, 4]\n    assert candidate('(()(())((())))') == [4]\n", "entry_point": "parse_nested_parens"}}}
{"id": "chat-3fadef08fe154b299ab04ab59796eb36", "object": "chat.completion", "created": 1737100205, "model": "Qwen2.5-7B-Instruct", "choices": [{"index": 0, "message": {"role": "assistant", "content": "Certainly! The function `filter_by_substring` should return a list of strings from the input list that contain the given substring. Here is the completed code:\n\n```python\nfrom typing import List\n\n\ndef filter_by_substring(strings: List[str], substring: str) -> List[str]:\n    \"\"\" Filter an input list of strings only for ones that contain given substring\n    >>> filter_by_substring([], 'a')\n    []\n    >>> filter_by_substring(['abc', 'bacd', 'cde', 'array'], 'a')\n    ['abc', 'bacd', 'array']\n    \"\"\"\n    return [s for s in strings if substring in s]\n\n\n# Test cases\nif __name__ == \"__main__\":\n    import doctest\n    doctest.testmod()\n```\n\nThis code defines the function `filter_by_substring` and includes a list comprehension to filter the strings. The `if __name__ == \"__main__\":` block is used to run the doctest test cases when the script is executed directly.", "tool_calls": []}, "logprobs": null, "finish_reason": "stop", "stop_reason": null}], "usage": {"prompt_tokens": 122, "total_tokens": 333, "completion_tokens": 211}, "prompt_logprobs": null, "model_spec": {"api_url": "http://127.0.0.1:8801/v1/chat/completions", "model_id": "Qwen2.5-7B-Instruct", "api_key": "EMPTY"}, "answer_id": "answer-9ceb90c5855c26e30da71825957ca028", "subset_name": "openai_humaneval", "raw_input": {"task_id": "HumanEval/7", "prompt": "from typing import List\n\n\ndef filter_by_substring(strings: List[str], substring: str) -> List[str]:\n    \"\"\" Filter an input list of strings only for ones that contain given substring\n    >>> filter_by_substring([], 'a')\n    []\n    >>> filter_by_substring(['abc', 'bacd', 'cde', 'array'], 'a')\n    ['abc', 'bacd', 'array']\n    \"\"\"\n", "canonical_solution": "    return [x for x in strings if substring in x]\n", "test": "\n\nMETADATA = {\n    'author': 'jt',\n    'dataset': 'test'\n}\n\n\ndef check(candidate):\n    assert candidate([], 'john') == []\n    assert candidate(['xxx', 'asd', 'xxy', 'john doe', 'xxxAAA', 'xxx'], 'xxx') == ['xxx', 'xxxAAA', 'xxx']\n    assert candidate(['xxx', 'asd', 'aaaxxy', 'john doe', 'xxxAAA', 'xxx'], 'xx') == ['xxx', 'aaaxxy', 'xxxAAA', 'xxx']\n    assert candidate(['grunt', 'trumpet', 'prune', 'gruesome'], 'run') == ['grunt', 'prune']\n", "entry_point": "filter_by_substring"}, "origin_prompt": {"data": ["Complete the following python code:\n\nfrom typing import List\n\n\ndef filter_by_substring(strings: List[str], substring: str) -> List[str]:\n    \"\"\" Filter an input list of strings only for ones that contain given substring\n    >>> filter_by_substring([], 'a')\n    []\n    >>> filter_by_substring(['abc', 'bacd', 'cde', 'array'], 'a')\n    ['abc', 'bacd', 'array']\n    \"\"\"\n"], "raw_input": {"task_id": "HumanEval/7", "prompt": "from typing import List\n\n\ndef filter_by_substring(strings: List[str], substring: str) -> List[str]:\n    \"\"\" Filter an input list of strings only for ones that contain given substring\n    >>> filter_by_substring([], 'a')\n    []\n    >>> filter_by_substring(['abc', 'bacd', 'cde', 'array'], 'a')\n    ['abc', 'bacd', 'array']\n    \"\"\"\n", "canonical_solution": "    return [x for x in strings if substring in x]\n", "test": "\n\nMETADATA = {\n    'author': 'jt',\n    'dataset': 'test'\n}\n\n\ndef check(candidate):\n    assert candidate([], 'john') == []\n    assert candidate(['xxx', 'asd', 'xxy', 'john doe', 'xxxAAA', 'xxx'], 'xxx') == ['xxx', 'xxxAAA', 'xxx']\n    assert candidate(['xxx', 'asd', 'aaaxxy', 'john doe', 'xxxAAA', 'xxx'], 'xx') == ['xxx', 'aaaxxy', 'xxxAAA', 'xxx']\n    assert candidate(['grunt', 'trumpet', 'prune', 'gruesome'], 'run') == ['grunt', 'prune']\n", "entry_point": "filter_by_substring"}}}
{"id": "chat-513f5746b0a84710bec1fc1a1ec6988c", "object": "chat.completion", "created": 1737100208, "model": "Qwen2.5-7B-Instruct", "choices": [{"index": 0, "message": {"role": "assistant", "content": "Certainly! Below is the completed Python code for the `sum_product` function:\n\n```python\nfrom typing import List, Tuple\n\n\ndef sum_product(numbers: List[int]) -> Tuple[int, int]:\n    \"\"\" For a given list of integers, return a tuple consisting of a sum and a product of all the integers in a list.\n    Empty sum should be equal to 0 and empty product should be equal to 1.\n    >>> sum_product([])\n    (0, 1)\n    >>> sum_product([1, 2, 3, 4])\n    (10, 24)\n    \"\"\"\n    if not numbers:\n        return (0, 1)\n    \n    total_sum = 0\n    total_product = 1\n    \n    for number in numbers:\n        total_sum += number\n        total_product *= number\n    \n    return (total_sum, total_product)\n\n# Test cases\nprint(sum_product([]))  # Expected output: (0, 1)\nprint(sum_product([1, 2, 3, 4]))  # Expected output: (10, 24)\n```\n\nThis code defines the `sum_product` function, which calculates the sum and product of a list of integers. It handles the case of an empty list by returning `(0, 1)`. For non-empty lists, it iterates through the list to compute the sum and product, and then returns a tuple containing these values.", "tool_calls": []}, "logprobs": null, "finish_reason": "stop", "stop_reason": null}], "usage": {"prompt_tokens": 141, "total_tokens": 438, "completion_tokens": 297}, "prompt_logprobs": null, "model_spec": {"api_url": "http://127.0.0.1:8801/v1/chat/completions", "model_id": "Qwen2.5-7B-Instruct", "api_key": "EMPTY"}, "answer_id": "answer-bce0e27212e13e47b963685c7c1a2f3a", "subset_name": "openai_humaneval", "raw_input": {"task_id": "HumanEval/8", "prompt": "from typing import List, Tuple\n\n\ndef sum_product(numbers: List[int]) -> Tuple[int, int]:\n    \"\"\" For a given list of integers, return a tuple consisting of a sum and a product of all the integers in a list.\n    Empty sum should be equal to 0 and empty product should be equal to 1.\n    >>> sum_product([])\n    (0, 1)\n    >>> sum_product([1, 2, 3, 4])\n    (10, 24)\n    \"\"\"\n", "canonical_solution": "    sum_value = 0\n    prod_value = 1\n\n    for n in numbers:\n        sum_value += n\n        prod_value *= n\n    return sum_value, prod_value\n", "test": "\n\nMETADATA = {\n    'author': 'jt',\n    'dataset': 'test'\n}\n\n\ndef check(candidate):\n    assert candidate([]) == (0, 1)\n    assert candidate([1, 1, 1]) == (3, 1)\n    assert candidate([100, 0]) == (100, 0)\n    assert candidate([3, 5, 7]) == (3 + 5 + 7, 3 * 5 * 7)\n    assert candidate([10]) == (10, 10)\n", "entry_point": "sum_product"}, "origin_prompt": {"data": ["Complete the following python code:\n\nfrom typing import List, Tuple\n\n\ndef sum_product(numbers: List[int]) -> Tuple[int, int]:\n    \"\"\" For a given list of integers, return a tuple consisting of a sum and a product of all the integers in a list.\n    Empty sum should be equal to 0 and empty product should be equal to 1.\n    >>> sum_product([])\n    (0, 1)\n    >>> sum_product([1, 2, 3, 4])\n    (10, 24)\n    \"\"\"\n"], "raw_input": {"task_id": "HumanEval/8", "prompt": "from typing import List, Tuple\n\n\ndef sum_product(numbers: List[int]) -> Tuple[int, int]:\n    \"\"\" For a given list of integers, return a tuple consisting of a sum and a product of all the integers in a list.\n    Empty sum should be equal to 0 and empty product should be equal to 1.\n    >>> sum_product([])\n    (0, 1)\n    >>> sum_product([1, 2, 3, 4])\n    (10, 24)\n    \"\"\"\n", "canonical_solution": "    sum_value = 0\n    prod_value = 1\n\n    for n in numbers:\n        sum_value += n\n        prod_value *= n\n    return sum_value, prod_value\n", "test": "\n\nMETADATA = {\n    'author': 'jt',\n    'dataset': 'test'\n}\n\n\ndef check(candidate):\n    assert candidate([]) == (0, 1)\n    assert candidate([1, 1, 1]) == (3, 1)\n    assert candidate([100, 0]) == (100, 0)\n    assert candidate([3, 5, 7]) == (3 + 5 + 7, 3 * 5 * 7)\n    assert candidate([10]) == (10, 10)\n", "entry_point": "sum_product"}}}
{"id": "chat-b034e71ab60f4d33b3757046e4d68cc4", "object": "chat.completion", "created": 1737100211, "model": "Qwen2.5-7B-Instruct", "choices": [{"index": 0, "message": {"role": "assistant", "content": "Certainly! To implement the `rolling_max` function, we can use a simple approach where we keep track of the current maximum as we iterate through the list. Here's the complete code:\n\n```python\nfrom typing import List, Tuple\n\n\ndef rolling_max(numbers: List[int]) -> List[int]:\n    \"\"\" From a given list of integers, generate a list of rolling maximum element found until given moment\n    in the sequence.\n    >>> rolling_max([1, 2, 3, 2, 3, 4, 2])\n    [1, 2, 3, 3, 3, 4, 4]\n    \"\"\"\n    if not numbers:\n        return []\n\n    rolling_max_list = [numbers[0]]  # Initialize the list with the first element\n    current_max = numbers[0]  # Initialize the current maximum\n\n    for num in numbers[1:]:\n        if num > current_max:\n            current_max = num\n        rolling_max_list.append(current_max)\n\n    return rolling_max_list\n\n\n# Test the function\nif __name__ == \"__main__\":\n    import doctest\n\n    doctest.testmod()\n```\n\n### Explanation:\n1. **Initialization**: We start by checking if the input list is empty. If it is, we return an empty list.\n2. **First Element**: We initialize the `rolling_max_list` with the first element of the input list and set `current_max` to the same value.\n3. **Iterate Through the List**: We iterate through the rest of the list starting from the second element.\n4. **Update Current Maximum**: If the current number is greater than `current_max`, we update `current_max`.\n5. **Append to Result List**: We append the current maximum to the `rolling_max_list`.\n6. **Return Result**: Finally, we return the `rolling_max_list`.\n\n### Testing:\nThe `doctest` module is used to verify the correctness of the function with the provided example. If you run the script, it will automatically test the function and print any failures or successes.", "tool_calls": []}, "logprobs": null, "finish_reason": "stop", "stop_reason": null}], "usage": {"prompt_tokens": 129, "total_tokens": 556, "completion_tokens": 427}, "prompt_logprobs": null, "model_spec": {"api_url": "http://127.0.0.1:8801/v1/chat/completions", "model_id": "Qwen2.5-7B-Instruct", "api_key": "EMPTY"}, "answer_id": "answer-4a7c8961f53f5c4e06e09830af859fec", "subset_name": "openai_humaneval", "raw_input": {"task_id": "HumanEval/9", "prompt": "from typing import List, Tuple\n\n\ndef rolling_max(numbers: List[int]) -> List[int]:\n    \"\"\" From a given list of integers, generate a list of rolling maximum element found until given moment\n    in the sequence.\n    >>> rolling_max([1, 2, 3, 2, 3, 4, 2])\n    [1, 2, 3, 3, 3, 4, 4]\n    \"\"\"\n", "canonical_solution": "    running_max = None\n    result = []\n\n    for n in numbers:\n        if running_max is None:\n            running_max = n\n        else:\n            running_max = max(running_max, n)\n\n        result.append(running_max)\n\n    return result\n", "test": "\n\nMETADATA = {\n    'author': 'jt',\n    'dataset': 'test'\n}\n\n\ndef check(candidate):\n    assert candidate([]) == []\n    assert candidate([1, 2, 3, 4]) == [1, 2, 3, 4]\n    assert candidate([4, 3, 2, 1]) == [4, 4, 4, 4]\n    assert candidate([3, 2, 3, 100, 3]) == [3, 3, 3, 100, 100]\n", "entry_point": "rolling_max"}, "origin_prompt": {"data": ["Complete the following python code:\n\nfrom typing import List, Tuple\n\n\ndef rolling_max(numbers: List[int]) -> List[int]:\n    \"\"\" From a given list of integers, generate a list of rolling maximum element found until given moment\n    in the sequence.\n    >>> rolling_max([1, 2, 3, 2, 3, 4, 2])\n    [1, 2, 3, 3, 3, 4, 4]\n    \"\"\"\n"], "raw_input": {"task_id": "HumanEval/9", "prompt": "from typing import List, Tuple\n\n\ndef rolling_max(numbers: List[int]) -> List[int]:\n    \"\"\" From a given list of integers, generate a list of rolling maximum element found until given moment\n    in the sequence.\n    >>> rolling_max([1, 2, 3, 2, 3, 4, 2])\n    [1, 2, 3, 3, 3, 4, 4]\n    \"\"\"\n", "canonical_solution": "    running_max = None\n    result = []\n\n    for n in numbers:\n        if running_max is None:\n            running_max = n\n        else:\n            running_max = max(running_max, n)\n\n        result.append(running_max)\n\n    return result\n", "test": "\n\nMETADATA = {\n    'author': 'jt',\n    'dataset': 'test'\n}\n\n\ndef check(candidate):\n    assert candidate([]) == []\n    assert candidate([1, 2, 3, 4]) == [1, 2, 3, 4]\n    assert candidate([4, 3, 2, 1]) == [4, 4, 4, 4]\n    assert candidate([3, 2, 3, 100, 3]) == [3, 3, 3, 100, 100]\n", "entry_point": "rolling_max"}}}
